Jaringan Saraf Tiruan dalam Mengidentifikasi Faktor-Faktor Penentu Kesiapan Belajar Anak pada Transisi ke Sekolah Dasar

Seri Arihta Br Sitepu; Novriyenni Novriyenni; Ratih Puspadini

doi:10.61132/neptunus.v3i3.990

Authors

Seri Arihta Br Sitepu Sekolah Tinggi Manajemen Informatika dan Komputer Kaputama
Novriyenni Novriyenni Sekolah Tinggi Manajemen Informatika dan Komputer Kaputama
Ratih Puspadini Sekolah Tinggi Manajemen Informatika dan Komputer Kaputama

DOI:

https://doi.org/10.61132/neptunus.v3i3.990

Keywords:

artificial neural network, child transition, decision tree, elementary school, learning readiness

Abstract

The transition of children from early childhood education to elementary school (SD) is a critical phase in their psychological and academic development. During this phase, children face significant challenges, including changes to a more structured learning environment and increasing academic demands. At SDN 055991 in Langkat Regency, this phenomenon is reflected in the difficulties experienced by some students, particularly with basic skills such as reading, writing, and arithmetic, as well as with socializing with peers. These difficulties can impact children's long-term academic and social development. This study aims to identify the key factors influencing children's learning readiness during this transition period, utilizing artificial intelligence (AI) technology. Specifically, this study uses Artificial Neural Networks (ANN) and Decision Trees as tools to analyze the data obtained. The use of this data-driven approach allows for a more in-depth analysis of the complex patterns and relationships between various variables that influence children's learning readiness, such as family factors, social environment, and students' basic skills. This study also references various previous studies demonstrating the effectiveness of backpropagation and Deep Learning algorithms in the context of education and student performance prediction. This approach is expected to provide more precise solutions for understanding children's learning readiness and provide a more accurate picture of the factors contributing to difficulties experienced by students in the transition to elementary school. The results of this study are expected to provide relevant recommendations for parents, educators, and education policymakers to support children's learning readiness and strengthen basic education policies that are adaptive to the needs of students in this digital era.

References

Apriyadi, A., Lubis, M. R., & Damanik, B. E. (2022). Penerapan algoritma C5.0 dalam menentukan tingkat pemahaman mahasiswa terhadap pembelajaran daring. Komputa: Jurnal Ilmiah Komputer dan Informatika, 11(1), 11–20. https://doi.org/10.34010/komputa.v11i1.7386

Ardiansyah, M., Syamsu, N., Rahmat, M. A., & Rismayanti. (2023). Analisis algoritma jaringan syaraf tiruan untuk mengidentifikasi bantuan beasiswa dengan model backpropagation. Jurnal Sistem dan Teknologi Informasi (JustIN), 11(2), 256.

Diponegoro, M. H., Kusumawardani, S. S., & Hidayah, I. (2021). Tinjauan pustaka sistematis: Implementasi metode deep learning pada prediksi kinerja murid. Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 10(2), 131–138. https://doi.org/10.22146/jnteti.v10i2.1417

Faqumala, D. A., & Pranoto, Y. K. S. (2020). Kesiapan anak masuk sekolah dasar (M. P. Moh. Nasrudin, Ed.; 1st ed.). Penerbit NEM.

Hafidz, D. I., Kartinah, Sukamto, & Mariyatun, S. (2023). Analisis minat belajar terhadap kesiapan belajar di kelas 3 SDN Sampangan 02. Jurnal Pendidikan dan Konseling, 5(2), 1639–1643. https://journal.universitaspahlawan.ac.id/index.php/jpdk/article/view/13099

Hidayatuloh, M. A., Kartika, K. P., & Permadani, D. F. H. (2022). Implementasi algoritma C4.5 untuk memprediksi capaian pembelajaran daring (Studi kasus siswa MAN 3 Blitar). Jurnal Algoritme, 3(1), 33–47. https://doi.org/10.35957/algoritme.v3i1.3292

Maiyuriska, R. (2022). Penerapan jaringan syaraf tiruan dengan algoritma backpropagation dalam memprediksi hasil panen gabah padi. Jurnal Informatika Ekonomi Bisnis, 4, 28–33. https://doi.org/10.37034/infeb.v4i1.115

Muhajirin, A., & Yasir, M. (2024). Penerapan jaringan saraf tiruan dengan metode backpropagation untuk memprediksi tingkat kelulusan mahasiswa perguruan tinggi. Digital Transformation Technology, 4(1), 214–224. https://doi.org/10.47709/digitech.v4i1.3810

Nahjan, M. R., Heryana, N., & Voutama, A. (2023). Implementasi Rapidminer dengan metode clustering K-means untuk analisa penjualan pada toko Oj Cell. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 101–104. https://doi.org/10.36040/jati.v7i1.6094

Ninta Liana Br Sitepu. (2021). Jaringan saraf tiruan memprediksi nilai pemelajaran siswa dengan metode backpropagation (Studi kasus: SMP Negeri 1 Salapian). Jurnal: Sistem Informasi, STMIK Kaputama Binjai.

Prasetyo, V. R., Lazuardi, H., Mulyono, A. A., & Lauw, C. (2021). Penerapan aplikasi RapidMiner untuk prediksi nilai tukar rupiah terhadap US Dollar dengan metode linear regression. Jurnal Nasional Teknologi dan Sistem Informasi, 7(1), 8–17. https://doi.org/10.25077/teknosi.v7i1.2021.8-17

Ramadhon, R. N., Ogi, A., Agung, A. P., Putra, R., Febrihartina, S. S., & Firdaus, U. (2024). Implementasi algoritma decision tree untuk klasifikasi pelanggan aktif atau tidak aktif pada data bank. Karimah Tauhid, 3(2), 1860–1874. https://doi.org/10.30997/karimahtauhid.v3i2.11952

Rizal, Martanto, & Wijaya, Y. A. (2022). Analisa dataset software defined network intrusion menggunakan algoritma deep learning H2O. JATI (Jurnal Mahasiswa Teknik Informatika), 6(2), 747–757. https://doi.org/10.36040/jati.v6i2.5724

Setio, P. B. N., Saputro, D. R. S., & Winarno, B. (2020). Klasifikasi dengan pohon keputusan berbasis algoritme C4.5. PRISMA, Prosiding Seminar Nasional Matematika, 3, 64–71.